Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for plm tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
x | A |
---|---|
conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level | The confidence level to use for the confidence interval
if |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Other plm tidiers:
augment.plm()
,
glance.plm()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
if (requireNamespace("plm", quietly = TRUE)) { library(plm) data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state", "year") ) summary(zz) tidy(zz) tidy(zz, conf.int = TRUE) tidy(zz, conf.int = TRUE, conf.level = 0.9) augment(zz) glance(zz) } #> # A tibble: 1 × 7 #> r.squared adj.r.squared statistic p.value deviance df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 0.941 0.937 3065. 0 1.11 764 816